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       "   FILE_ID  RECORD_ROW           CARD_ID  CARD_TYPE  TRADE_TYPE  \\\n",
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       "\n",
       "   TRADE_MONEY  ...  READER_FILE_NAME  MODE_ID               CLEARING_DATE  \\\n",
       "0            0  ...               NaN      0.0  2015-08-01-00.00.00.000000   \n",
       "1            0  ...               NaN      0.0  2015-08-01-00.00.00.000000   \n",
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      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data_08=pd.read_csv('data/acc_08_final.csv')\n",
    "data_08.head()"
   ]
  },
  {
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       "4  4448147           2  66688876156         88          21            133   \n",
       "\n",
       "                   TRADE_DATE  TERMINAL_ID  OPERATOR  TRADE_MONEY  ...  \\\n",
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       "1  2015-09-01-03.00.24.000000     14541012         0            0  ...   \n",
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       "4  2015-09-01-05.17.05.000000     13340012         0            0  ...   \n",
       "\n",
       "   READER_FILE_NAME  MODE_ID               CLEARING_DATE  \\\n",
       "0               NaN      0.0  2015-08-31-00.00.00.000000   \n",
       "1               NaN      0.0  2015-08-31-00.00.00.000000   \n",
       "2               NaN      0.0  2015-09-01-00.00.00.000000   \n",
       "3               NaN      0.0  2015-09-01-00.00.00.000000   \n",
       "4               NaN      0.0  2015-09-01-00.00.00.000000   \n",
       "\n",
       "                 RECEIVE_DATE  DAY  RUN_TYPE  PAY_CARD_ID PURSE_FLAG  \\\n",
       "0  2015-09-01-01.31.49.761020   31       NaN          NaN        NaN   \n",
       "1  2015-09-01-03.16.36.038025   31       NaN          NaN        NaN   \n",
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       "\n",
       "  CITY_CODE  INDUSTRY_CODE  \n",
       "0      4500              3  \n",
       "1      4500              3  \n",
       "2      4500              3  \n",
       "3      4500              3  \n",
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       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_09=pd.read_csv('data/acc_09_final.csv')\n",
    "data_09.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4487977</td>\n",
       "      <td>2</td>\n",
       "      <td>66687877255</td>\n",
       "      <td>88</td>\n",
       "      <td>22</td>\n",
       "      <td>155</td>\n",
       "      <td>2015-10-01-05.33.56.000000</td>\n",
       "      <td>15542009</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-10-01-00.00.00.000000</td>\n",
       "      <td>2015-10-01-05.50.02.137135</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4487980</td>\n",
       "      <td>112</td>\n",
       "      <td>66687877243</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>151</td>\n",
       "      <td>2015-10-01-05.41.38.000000</td>\n",
       "      <td>15142004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-10-01-00.00.00.000000</td>\n",
       "      <td>2015-10-01-06.05.16.672232</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4487979</td>\n",
       "      <td>2</td>\n",
       "      <td>66446666919</td>\n",
       "      <td>88</td>\n",
       "      <td>22</td>\n",
       "      <td>125</td>\n",
       "      <td>2015-10-01-05.41.44.000000</td>\n",
       "      <td>12541003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-10-01-00.00.00.000000</td>\n",
       "      <td>2015-10-01-06.05.15.712620</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4487980</td>\n",
       "      <td>80</td>\n",
       "      <td>66687877255</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>155</td>\n",
       "      <td>2015-10-01-05.41.57.000000</td>\n",
       "      <td>15540011</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-10-01-00.00.00.000000</td>\n",
       "      <td>2015-10-01-06.05.16.544336</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FILE_ID  RECORD_ROW      CARD_ID  CARD_TYPE  TRADE_TYPE  TRADE_ADDRESS  \\\n",
       "0  4487978           2  66687877255         88          21            155   \n",
       "1  4487977           2  66687877255         88          22            155   \n",
       "2  4487980         112  66687877243         88          21            151   \n",
       "3  4487979           2  66446666919         88          22            125   \n",
       "4  4487980          80  66687877255         88          21            155   \n",
       "\n",
       "                   TRADE_DATE  TERMINAL_ID  OPERATOR  TRADE_MONEY  ...  \\\n",
       "0  2015-10-01-05.33.04.000000     15542031         0            0  ...   \n",
       "1  2015-10-01-05.33.56.000000     15542009         0            0  ...   \n",
       "2  2015-10-01-05.41.38.000000     15142004         0            0  ...   \n",
       "3  2015-10-01-05.41.44.000000     12541003         0            0  ...   \n",
       "4  2015-10-01-05.41.57.000000     15540011         0            0  ...   \n",
       "\n",
       "   READER_FILE_NAME  MODE_ID               CLEARING_DATE  \\\n",
       "0               NaN      0.0  2015-10-01-00.00.00.000000   \n",
       "1               NaN      0.0  2015-10-01-00.00.00.000000   \n",
       "2               NaN      0.0  2015-10-01-00.00.00.000000   \n",
       "3               NaN      0.0  2015-10-01-00.00.00.000000   \n",
       "4               NaN      0.0  2015-10-01-00.00.00.000000   \n",
       "\n",
       "                 RECEIVE_DATE  DAY  RUN_TYPE  PAY_CARD_ID PURSE_FLAG  \\\n",
       "0  2015-10-01-05.50.33.867121    1       NaN          NaN        NaN   \n",
       "1  2015-10-01-05.50.02.137135    1       NaN          NaN        NaN   \n",
       "2  2015-10-01-06.05.16.672232    1       NaN          NaN        NaN   \n",
       "3  2015-10-01-06.05.15.712620    1       NaN          NaN        NaN   \n",
       "4  2015-10-01-06.05.16.544336    1       NaN          NaN        NaN   \n",
       "\n",
       "  CITY_CODE  INDUSTRY_CODE  \n",
       "0      4500              3  \n",
       "1      4500              3  \n",
       "2      4500              3  \n",
       "3      4500              3  \n",
       "4      4500              3  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_10=pd.read_csv('data/acc_10_final.csv')\n",
    "data_10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <td>4500</td>\n",
       "      <td>3</td>\n",
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       "      <th>1</th>\n",
       "      <td>4530163</td>\n",
       "      <td>9</td>\n",
       "      <td>66446666059</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>157</td>\n",
       "      <td>2015-11-01-05.42.36.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>10</td>\n",
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       "      <td>88</td>\n",
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       "      <td>2015-11-01-05.42.37.000000</td>\n",
       "      <td>15740012</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-11-01-00.00.00.000000</td>\n",
       "      <td>2015-11-01-05.53.44.306787</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4530163</td>\n",
       "      <td>2</td>\n",
       "      <td>66446666959</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>123</td>\n",
       "      <td>2015-11-01-05.42.54.000000</td>\n",
       "      <td>12340006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-11-01-00.00.00.000000</td>\n",
       "      <td>2015-11-01-05.53.44.271787</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4530163</td>\n",
       "      <td>6</td>\n",
       "      <td>66688877300</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>155</td>\n",
       "      <td>2015-11-01-05.42.55.000000</td>\n",
       "      <td>15542031</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-11-01-00.00.00.000000</td>\n",
       "      <td>2015-11-01-05.53.44.290091</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FILE_ID  RECORD_ROW      CARD_ID  CARD_TYPE  TRADE_TYPE  TRADE_ADDRESS  \\\n",
       "0  4530161           2  66688877326         88          21            127   \n",
       "1  4530163           9  66446666059         88          21            157   \n",
       "2  4530163          10  66688877360         88          21            157   \n",
       "3  4530163           2  66446666959         88          21            123   \n",
       "4  4530163           6  66688877300         88          21            155   \n",
       "\n",
       "                   TRADE_DATE  TERMINAL_ID  OPERATOR  TRADE_MONEY  ...  \\\n",
       "0  2015-11-01-05.14.43.000000     12740011         0            0  ...   \n",
       "1  2015-11-01-05.42.36.000000     15742001         0            0  ...   \n",
       "2  2015-11-01-05.42.37.000000     15740012         0            0  ...   \n",
       "3  2015-11-01-05.42.54.000000     12340006         0            0  ...   \n",
       "4  2015-11-01-05.42.55.000000     15542031         0            0  ...   \n",
       "\n",
       "   READER_FILE_NAME  MODE_ID               CLEARING_DATE  \\\n",
       "0               NaN      0.0  2015-11-01-00.00.00.000000   \n",
       "1               NaN      0.0  2015-11-01-00.00.00.000000   \n",
       "2               NaN      0.0  2015-11-01-00.00.00.000000   \n",
       "3               NaN      0.0  2015-11-01-00.00.00.000000   \n",
       "4               NaN      0.0  2015-11-01-00.00.00.000000   \n",
       "\n",
       "                 RECEIVE_DATE  DAY  RUN_TYPE  PAY_CARD_ID PURSE_FLAG  \\\n",
       "0  2015-11-01-05.23.48.394773    1       NaN          NaN        NaN   \n",
       "1  2015-11-01-05.53.44.302798    1       NaN          NaN        NaN   \n",
       "2  2015-11-01-05.53.44.306787    1       NaN          NaN        NaN   \n",
       "3  2015-11-01-05.53.44.271787    1       NaN          NaN        NaN   \n",
       "4  2015-11-01-05.53.44.290091    1       NaN          NaN        NaN   \n",
       "\n",
       "  CITY_CODE  INDUSTRY_CODE  \n",
       "0      4500              3  \n",
       "1      4500              3  \n",
       "2      4500              3  \n",
       "3      4500              3  \n",
       "4      4500              3  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_11=pd.read_csv('data/acc_11_final.csv')\n",
    "data_11.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以看出8月，9月，10月，11月客流量是递增的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121     911645\n",
       "123     327285\n",
       "125     671127\n",
       "127     645753\n",
       "129     435859\n",
       "131     503195\n",
       "133     639721\n",
       "135    1629278\n",
       "137    1944886\n",
       "139     667543\n",
       "141    1104445\n",
       "143     659050\n",
       "145     585817\n",
       "147     762984\n",
       "149     489094\n",
       "151     426046\n",
       "153     535656\n",
       "155    1309968\n",
       "157      20776\n",
       "159    1336828\n",
       "Name: TRADE_ADDRESS, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address_08=data_08[\"TRADE_ADDRESS\"].value_counts().sort_index()\n",
    "address_09=data_09[\"TRADE_ADDRESS\"].value_counts().sort_index()\n",
    "address_10=data_10[\"TRADE_ADDRESS\"].value_counts().sort_index()\n",
    "address_11=data_11[\"TRADE_ADDRESS\"].value_counts().sort_index()\n",
    "address_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#分析每个月份不同闸机的客流量分布\n",
    "plt.title(\"address count per month\")\n",
    "plt.plot(address_08,color=\"g\",label=\"08\")\n",
    "plt.plot(address_09,color=\"blue\",label=\"09\")\n",
    "plt.plot(address_10,color=\"r\",label=\"10\")\n",
    "plt.plot(address_11,color=\"y\",label=\"11\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建立新label值，提取日期到月、日"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_08[\"TRADE_DATE_DAY\"]=data_08[\"TRADE_DATE\"].apply(lambda x:x[:10])\n",
    "data_09[\"TRADE_DATE_DAY\"]=data_09[\"TRADE_DATE\"].apply(lambda x:x[:10])\n",
    "data_10[\"TRADE_DATE_DAY\"]=data_10[\"TRADE_DATE\"].apply(lambda x:x[:10])\n",
    "data_11[\"TRADE_DATE_DAY\"]=data_11[\"TRADE_DATE\"].apply(lambda x:x[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_date_08=data_08[\"TRADE_DATE_DAY\"].value_counts().sort_index()\n",
    "trade_date_09=data_09[\"TRADE_DATE_DAY\"].value_counts().sort_index()\n",
    "trade_date_10=data_10[\"TRADE_DATE_DAY\"].value_counts().sort_index()\n",
    "trade_date_11=data_11[\"TRADE_DATE_DAY\"].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析每个月份不同日期的客流量分布\n",
    "fig=plt.figure(figsize=(12,8))\n",
    "\n",
    "ax1=fig.add_subplot(2,2,1)\n",
    "plt.plot(trade_date_08,color=\"g\",label=\"08\")\n",
    "plt.legend()\n",
    "ax2=fig.add_subplot(2,2,2)\n",
    "plt.plot(trade_date_09,color=\"b\",label=\"09\")\n",
    "plt.legend()\n",
    "ax3=fig.add_subplot(2,2,3)\n",
    "plt.plot(trade_date_10,color=\"r\",label=\"10\")\n",
    "plt.legend()\n",
    "ax2=fig.add_subplot(2,2,4)\n",
    "plt.plot(trade_date_11,color=\"y\",label=\"11\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为8月处于学生暑假阶段，所以工作日和双休日峰值差距并没有太大，在9,10,11月时可以明显看出休息日和工作日的差别（波谷）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 添加新label，转化对应日期为星期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import calendar\n",
    "from datetime import datetime\n",
    "\n",
    "calendar.day_name[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将字符串格式转化为timestap\n",
    "data_08[\"TRADE_DATE_DAY\"]=pd.to_datetime(data_08[\"TRADE_DATE_DAY\"],format=\"%Y-%m-%d\")\n",
    "data_09[\"TRADE_DATE_DAY\"]=pd.to_datetime(data_09[\"TRADE_DATE_DAY\"],format=\"%Y-%m-%d\")\n",
    "data_10[\"TRADE_DATE_DAY\"]=pd.to_datetime(data_10[\"TRADE_DATE_DAY\"],format=\"%Y-%m-%d\")\n",
    "data_11[\"TRADE_DATE_DAY\"]=pd.to_datetime(data_11[\"TRADE_DATE_DAY\"],format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILE_ID</th>\n",
       "      <th>RECORD_ROW</th>\n",
       "      <th>CARD_ID</th>\n",
       "      <th>CARD_TYPE</th>\n",
       "      <th>TRADE_TYPE</th>\n",
       "      <th>TRADE_ADDRESS</th>\n",
       "      <th>TRADE_DATE</th>\n",
       "      <th>TERMINAL_ID</th>\n",
       "      <th>OPERATOR</th>\n",
       "      <th>TRADE_MONEY</th>\n",
       "      <th>...</th>\n",
       "      <th>CLEARING_DATE</th>\n",
       "      <th>RECEIVE_DATE</th>\n",
       "      <th>DAY</th>\n",
       "      <th>RUN_TYPE</th>\n",
       "      <th>PAY_CARD_ID</th>\n",
       "      <th>PURSE_FLAG</th>\n",
       "      <th>CITY_CODE</th>\n",
       "      <th>INDUSTRY_CODE</th>\n",
       "      <th>TRADE_DATE_DAY</th>\n",
       "      <th>WEEKDAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4409252</td>\n",
       "      <td>5</td>\n",
       "      <td>66446666904</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>157</td>\n",
       "      <td>2015-08-01-05.42.28.000000</td>\n",
       "      <td>15742001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-08-01-00.00.00.000000</td>\n",
       "      <td>2015-08-01-05.58.00.167016</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4409252</td>\n",
       "      <td>2</td>\n",
       "      <td>66446666929</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>155</td>\n",
       "      <td>2015-08-01-05.42.40.000000</td>\n",
       "      <td>15542009</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-08-01-00.00.00.000000</td>\n",
       "      <td>2015-08-01-05.58.00.139164</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4409252</td>\n",
       "      <td>7</td>\n",
       "      <td>66666279182</td>\n",
       "      <td>98</td>\n",
       "      <td>21</td>\n",
       "      <td>149</td>\n",
       "      <td>2015-08-01-05.44.55.000000</td>\n",
       "      <td>14940010</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-08-01-00.00.00.000000</td>\n",
       "      <td>2015-08-01-05.58.00.175207</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4409255</td>\n",
       "      <td>14</td>\n",
       "      <td>6371626867758334</td>\n",
       "      <td>66</td>\n",
       "      <td>21</td>\n",
       "      <td>155</td>\n",
       "      <td>2015-08-01-05.46.07.000000</td>\n",
       "      <td>15542010</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-08-01-00.00.00.000000</td>\n",
       "      <td>2015-08-01-06.13.12.752374</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4409252</td>\n",
       "      <td>3</td>\n",
       "      <td>66446666911</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>121</td>\n",
       "      <td>2015-08-01-05.46.33.000000</td>\n",
       "      <td>12140009</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-08-01-00.00.00.000000</td>\n",
       "      <td>2015-08-01-05.58.00.158549</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FILE_ID  RECORD_ROW           CARD_ID  CARD_TYPE  TRADE_TYPE  \\\n",
       "0  4409252           5       66446666904         88          21   \n",
       "1  4409252           2       66446666929         88          21   \n",
       "2  4409252           7       66666279182         98          21   \n",
       "3  4409255          14  6371626867758334         66          21   \n",
       "4  4409252           3       66446666911         88          21   \n",
       "\n",
       "   TRADE_ADDRESS                  TRADE_DATE  TERMINAL_ID  OPERATOR  \\\n",
       "0            157  2015-08-01-05.42.28.000000     15742001         0   \n",
       "1            155  2015-08-01-05.42.40.000000     15542009         0   \n",
       "2            149  2015-08-01-05.44.55.000000     14940010         0   \n",
       "3            155  2015-08-01-05.46.07.000000     15542010         0   \n",
       "4            121  2015-08-01-05.46.33.000000     12140009         0   \n",
       "\n",
       "   TRADE_MONEY  ...               CLEARING_DATE                RECEIVE_DATE  \\\n",
       "0            0  ...  2015-08-01-00.00.00.000000  2015-08-01-05.58.00.167016   \n",
       "1            0  ...  2015-08-01-00.00.00.000000  2015-08-01-05.58.00.139164   \n",
       "2            0  ...  2015-08-01-00.00.00.000000  2015-08-01-05.58.00.175207   \n",
       "3            0  ...  2015-08-01-00.00.00.000000  2015-08-01-06.13.12.752374   \n",
       "4            0  ...  2015-08-01-00.00.00.000000  2015-08-01-05.58.00.158549   \n",
       "\n",
       "   DAY  RUN_TYPE  PAY_CARD_ID  PURSE_FLAG  CITY_CODE INDUSTRY_CODE  \\\n",
       "0    1       NaN          NaN         NaN       4500             3   \n",
       "1    1       NaN          NaN         NaN       4500             3   \n",
       "2    1       NaN          NaN         NaN          0             0   \n",
       "3    1       NaN          NaN         NaN       4500             0   \n",
       "4    1       NaN          NaN         NaN       4500             3   \n",
       "\n",
       "  TRADE_DATE_DAY   WEEKDAY  \n",
       "0     2015-08-01  Saturday  \n",
       "1     2015-08-01  Saturday  \n",
       "2     2015-08-01  Saturday  \n",
       "3     2015-08-01  Saturday  \n",
       "4     2015-08-01  Saturday  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#日期转星期函数\n",
    "def get_weekday(dataString):\n",
    "    week_day=dataString.weekday()\n",
    "    return (calendar.day_name[week_day])\n",
    "\n",
    "#获取date整列数据的星期\n",
    "data_08[\"WEEKDAY\"]=data_08[\"TRADE_DATE_DAY\"].apply(get_weekday)\n",
    "data_08.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "   FILE_ID  RECORD_ROW      CARD_ID  CARD_TYPE  TRADE_TYPE  TRADE_ADDRESS  \\\n",
       "0  4448140           2  66496666621         88          22            121   \n",
       "1  4448142           2  66496666069         88          22            145   \n",
       "2  4448144           2  66446666980         88          21            133   \n",
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       "4  4448147           2  66688876156         88          21            133   \n",
       "\n",
       "                   TRADE_DATE  TERMINAL_ID  OPERATOR  TRADE_MONEY  ...  \\\n",
       "0  2015-09-01-01.20.46.000000     12141010         0            0  ...   \n",
       "1  2015-09-01-03.00.24.000000     14541012         0            0  ...   \n",
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       "4  2015-09-01-05.17.05.000000     13340012         0            0  ...   \n",
       "\n",
       "                CLEARING_DATE                RECEIVE_DATE  DAY  RUN_TYPE  \\\n",
       "0  2015-08-31-00.00.00.000000  2015-09-01-01.31.49.761020   31       NaN   \n",
       "1  2015-08-31-00.00.00.000000  2015-09-01-03.16.36.038025   31       NaN   \n",
       "2  2015-09-01-00.00.00.000000  2015-09-01-04.46.54.528045    1       NaN   \n",
       "3  2015-09-01-00.00.00.000000  2015-09-01-05.01.35.832650    1       NaN   \n",
       "4  2015-09-01-00.00.00.000000  2015-09-01-05.46.39.913754    1       NaN   \n",
       "\n",
       "   PAY_CARD_ID  PURSE_FLAG  CITY_CODE INDUSTRY_CODE TRADE_DATE_DAY  WEEKDAY  \n",
       "0          NaN         NaN       4500             3     2015-09-01  Tuesday  \n",
       "1          NaN         NaN       4500             3     2015-09-01  Tuesday  \n",
       "2          NaN         NaN       4500             3     2015-09-01  Tuesday  \n",
       "3          NaN         NaN       4500             3     2015-09-01  Tuesday  \n",
       "4          NaN         NaN       4500             3     2015-09-01  Tuesday  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#如法炮制\n",
    "data_09[\"WEEKDAY\"]=data_09[\"TRADE_DATE_DAY\"].apply(get_weekday) \n",
    "data_09.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "   FILE_ID  RECORD_ROW      CARD_ID  CARD_TYPE  TRADE_TYPE  TRADE_ADDRESS  \\\n",
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       "4  4487980          80  66687877255         88          21            155   \n",
       "\n",
       "                   TRADE_DATE  TERMINAL_ID  OPERATOR  TRADE_MONEY  ...  \\\n",
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       "1  2015-10-01-05.33.56.000000     15542009         0            0  ...   \n",
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       "4  2015-10-01-05.41.57.000000     15540011         0            0  ...   \n",
       "\n",
       "                CLEARING_DATE                RECEIVE_DATE  DAY  RUN_TYPE  \\\n",
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       "4  2015-10-01-00.00.00.000000  2015-10-01-06.05.16.544336    1       NaN   \n",
       "\n",
       "   PAY_CARD_ID  PURSE_FLAG  CITY_CODE INDUSTRY_CODE TRADE_DATE_DAY   WEEKDAY  \n",
       "0          NaN         NaN       4500             3     2015-10-01  Thursday  \n",
       "1          NaN         NaN       4500             3     2015-10-01  Thursday  \n",
       "2          NaN         NaN       4500             3     2015-10-01  Thursday  \n",
       "3          NaN         NaN       4500             3     2015-10-01  Thursday  \n",
       "4          NaN         NaN       4500             3     2015-10-01  Thursday  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#如法炮制\n",
    "data_10[\"WEEKDAY\"]=data_10[\"TRADE_DATE_DAY\"].apply(get_weekday)\n",
    "data_10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILE_ID</th>\n",
       "      <th>RECORD_ROW</th>\n",
       "      <th>CARD_ID</th>\n",
       "      <th>CARD_TYPE</th>\n",
       "      <th>TRADE_TYPE</th>\n",
       "      <th>TRADE_ADDRESS</th>\n",
       "      <th>TRADE_DATE</th>\n",
       "      <th>TERMINAL_ID</th>\n",
       "      <th>OPERATOR</th>\n",
       "      <th>TRADE_MONEY</th>\n",
       "      <th>...</th>\n",
       "      <th>CLEARING_DATE</th>\n",
       "      <th>RECEIVE_DATE</th>\n",
       "      <th>DAY</th>\n",
       "      <th>RUN_TYPE</th>\n",
       "      <th>PAY_CARD_ID</th>\n",
       "      <th>PURSE_FLAG</th>\n",
       "      <th>CITY_CODE</th>\n",
       "      <th>INDUSTRY_CODE</th>\n",
       "      <th>TRADE_DATE_DAY</th>\n",
       "      <th>WEEKDAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4530161</td>\n",
       "      <td>2</td>\n",
       "      <td>66688877326</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>127</td>\n",
       "      <td>2015-11-01-05.14.43.000000</td>\n",
       "      <td>12740011</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-11-01-00.00.00.000000</td>\n",
       "      <td>2015-11-01-05.23.48.394773</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4530163</td>\n",
       "      <td>9</td>\n",
       "      <td>66446666059</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>157</td>\n",
       "      <td>2015-11-01-05.42.36.000000</td>\n",
       "      <td>15742001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-11-01-00.00.00.000000</td>\n",
       "      <td>2015-11-01-05.53.44.302798</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4530163</td>\n",
       "      <td>10</td>\n",
       "      <td>66688877360</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>157</td>\n",
       "      <td>2015-11-01-05.42.37.000000</td>\n",
       "      <td>15740012</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-11-01-00.00.00.000000</td>\n",
       "      <td>2015-11-01-05.53.44.306787</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4530163</td>\n",
       "      <td>2</td>\n",
       "      <td>66446666959</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>123</td>\n",
       "      <td>2015-11-01-05.42.54.000000</td>\n",
       "      <td>12340006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-11-01-00.00.00.000000</td>\n",
       "      <td>2015-11-01-05.53.44.271787</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4530163</td>\n",
       "      <td>6</td>\n",
       "      <td>66688877300</td>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "      <td>155</td>\n",
       "      <td>2015-11-01-05.42.55.000000</td>\n",
       "      <td>15542031</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-11-01-00.00.00.000000</td>\n",
       "      <td>2015-11-01-05.53.44.290091</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FILE_ID  RECORD_ROW      CARD_ID  CARD_TYPE  TRADE_TYPE  TRADE_ADDRESS  \\\n",
       "0  4530161           2  66688877326         88          21            127   \n",
       "1  4530163           9  66446666059         88          21            157   \n",
       "2  4530163          10  66688877360         88          21            157   \n",
       "3  4530163           2  66446666959         88          21            123   \n",
       "4  4530163           6  66688877300         88          21            155   \n",
       "\n",
       "                   TRADE_DATE  TERMINAL_ID  OPERATOR  TRADE_MONEY  ...  \\\n",
       "0  2015-11-01-05.14.43.000000     12740011         0            0  ...   \n",
       "1  2015-11-01-05.42.36.000000     15742001         0            0  ...   \n",
       "2  2015-11-01-05.42.37.000000     15740012         0            0  ...   \n",
       "3  2015-11-01-05.42.54.000000     12340006         0            0  ...   \n",
       "4  2015-11-01-05.42.55.000000     15542031         0            0  ...   \n",
       "\n",
       "                CLEARING_DATE                RECEIVE_DATE  DAY  RUN_TYPE  \\\n",
       "0  2015-11-01-00.00.00.000000  2015-11-01-05.23.48.394773    1       NaN   \n",
       "1  2015-11-01-00.00.00.000000  2015-11-01-05.53.44.302798    1       NaN   \n",
       "2  2015-11-01-00.00.00.000000  2015-11-01-05.53.44.306787    1       NaN   \n",
       "3  2015-11-01-00.00.00.000000  2015-11-01-05.53.44.271787    1       NaN   \n",
       "4  2015-11-01-00.00.00.000000  2015-11-01-05.53.44.290091    1       NaN   \n",
       "\n",
       "   PAY_CARD_ID  PURSE_FLAG  CITY_CODE INDUSTRY_CODE TRADE_DATE_DAY  WEEKDAY  \n",
       "0          NaN         NaN       4500             3     2015-11-01   Sunday  \n",
       "1          NaN         NaN       4500             3     2015-11-01   Sunday  \n",
       "2          NaN         NaN       4500             3     2015-11-01   Sunday  \n",
       "3          NaN         NaN       4500             3     2015-11-01   Sunday  \n",
       "4          NaN         NaN       4500             3     2015-11-01   Sunday  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#如法炮制\n",
    "data_11[\"WEEKDAY\"]=data_11[\"TRADE_DATE_DAY\"].apply(get_weekday)\n",
    "data_11.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_08[\"WEEKDAY\"].value_counts().sort_index()\n",
    "type(data_08[\"WEEKDAY\"].value_counts())\n",
    "data_08_weekday=data_08[\"WEEKDAY\"].value_counts()\n",
    "data_09_weekday=data_09[\"WEEKDAY\"].value_counts()\n",
    "data_10_weekday=data_10[\"WEEKDAY\"].value_counts()\n",
    "data_11_weekday=data_11[\"WEEKDAY\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1851812"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_08_weekday['Friday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制柱状图，不同月份每周客流量\n",
    "fig=plt.figure(figsize=(12,8))\n",
    "ax1=fig.add_subplot(2,2,1)\n",
    "x_data=[\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\",\"Sunday\"]\n",
    "y_data=[data_08_weekday[\"Monday\"],data_08_weekday[\"Tuesday\"],data_08_weekday[\"Wednesday\"],data_08_weekday[\"Thursday\"],data_08_weekday[\"Friday\"],data_08_weekday[\"Saturday\"],data_08_weekday[\"Sunday\"]]\n",
    "#绘图\n",
    "plt.bar(x=x_data,height=y_data,label='08',color='r',alpha=.8)\n",
    "plt.title(\"Count per week\")\n",
    "plt.legend()\n",
    "\n",
    "ax2=fig.add_subplot(2,2,2)\n",
    "x_data=[\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\",\"Sunday\"]\n",
    "y_data=[data_09_weekday[\"Monday\"],data_09_weekday[\"Tuesday\"],data_09_weekday[\"Wednesday\"],data_09_weekday[\"Thursday\"],data_09_weekday[\"Friday\"],data_09_weekday[\"Saturday\"],data_09_weekday[\"Sunday\"]]\n",
    "#绘图\n",
    "plt.bar(x=x_data,height=y_data,label='09',alpha=.8)\n",
    "plt.title(\"Count per week\")\n",
    "plt.legend()\n",
    "\n",
    "ax3=fig.add_subplot(2,2,3)\n",
    "x_data=[\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\",\"Sunday\"]\n",
    "y_data=[data_10_weekday[\"Monday\"],data_10_weekday[\"Tuesday\"],data_10_weekday[\"Wednesday\"],data_10_weekday[\"Thursday\"],data_10_weekday[\"Friday\"],data_10_weekday[\"Saturday\"],data_10_weekday[\"Sunday\"]]\n",
    "#绘图\n",
    "plt.bar(x=x_data,height=y_data,label='10',color='g',alpha=.8)\n",
    "plt.title(\"Count per week\")\n",
    "plt.legend()\n",
    "\n",
    "ax4=fig.add_subplot(2,2,4)\n",
    "x_data=[\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\",\"Sunday\"]\n",
    "y_data=[data_11_weekday[\"Monday\"],data_11_weekday[\"Tuesday\"],data_11_weekday[\"Wednesday\"],data_11_weekday[\"Thursday\"],data_11_weekday[\"Friday\"],data_11_weekday[\"Saturday\"],data_11_weekday[\"Sunday\"]]\n",
    "#绘图\n",
    "plt.bar(x=x_data,height=y_data,label='11',color='y',alpha=.8)\n",
    "plt.title(\"Count per week\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13532169 entries, 0 to 13532168\n",
      "Data columns (total 43 columns):\n",
      "FILE_ID             int64\n",
      "RECORD_ROW          int64\n",
      "CARD_ID             object\n",
      "CARD_TYPE           int64\n",
      "TRADE_TYPE          int64\n",
      "TRADE_ADDRESS       int64\n",
      "TRADE_DATE          object\n",
      "TERMINAL_ID         int64\n",
      "OPERATOR            int64\n",
      "TRADE_MONEY         int64\n",
      "TRADE_VALUE         int64\n",
      "CURRENT_VALUE       int64\n",
      "FOREGIFT            int64\n",
      "CHARGE              int64\n",
      "CARD_SN             int64\n",
      "TERMINAL_SN         int64\n",
      "TERMINAL_SUM        int64\n",
      "TAC                 object\n",
      "START_DATE          object\n",
      "START_ADDRESS       float64\n",
      "START_TERMINAL      float64\n",
      "TRADE_TYPE_EX       int64\n",
      "DESTINATION         float64\n",
      "SAM_ID              object\n",
      "APP_TYPE            int64\n",
      "SAM_SN              int64\n",
      "CARD_IDM            object\n",
      "READER_SN           float64\n",
      "READER_SUM          float64\n",
      "CARD_TYPE_EX        int64\n",
      "CARD_OWNER          float64\n",
      "READER_FILE_NAME    float64\n",
      "MODE_ID             float64\n",
      "CLEARING_DATE       object\n",
      "RECEIVE_DATE        object\n",
      "DAY                 int64\n",
      "RUN_TYPE            float64\n",
      "PAY_CARD_ID         float64\n",
      "PURSE_FLAG          float64\n",
      "CITY_CODE           int64\n",
      "INDUSTRY_CODE       int64\n",
      "TRADE_DATE_DAY      datetime64[ns]\n",
      "WEEKDAY             object\n",
      "dtypes: datetime64[ns](1), float64(11), int64(22), object(9)\n",
      "memory usage: 4.3+ GB\n"
     ]
    }
   ],
   "source": [
    "data_08.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22628"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_08.loc[(data_08[\"TRADE_DATE_DAY\"]==\"2015-08-01\")&(data_08[\"TRADE_ADDRESS\"]==121)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRADE_DATE_DAY</th>\n",
       "      <th>TRADE_ADDRESS</th>\n",
       "      <th>WEEKDAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>157</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>155</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>149</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>155</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>121</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  TRADE_DATE_DAY  TRADE_ADDRESS   WEEKDAY\n",
       "0     2015-08-01            157  Saturday\n",
       "1     2015-08-01            155  Saturday\n",
       "2     2015-08-01            149  Saturday\n",
       "3     2015-08-01            155  Saturday\n",
       "4     2015-08-01            121  Saturday"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_08_re=data_08[['TRADE_DATE_DAY','TRADE_ADDRESS','WEEKDAY']]\n",
    "data_08_re.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRADE_DATE_DAY</th>\n",
       "      <th>TRADE_ADDRESS</th>\n",
       "      <th>WEEKDAY</th>\n",
       "      <th>val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>157</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>155</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>149</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>155</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>121</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  TRADE_DATE_DAY  TRADE_ADDRESS   WEEKDAY  val\n",
       "0     2015-08-01            157  Saturday    1\n",
       "1     2015-08-01            155  Saturday    1\n",
       "2     2015-08-01            149  Saturday    1\n",
       "3     2015-08-01            155  Saturday    1\n",
       "4     2015-08-01            121  Saturday    1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_08_re['val']=1\n",
    "data_08_re.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRADE_DATE_DAY</th>\n",
       "      <th>TRADE_ADDRESS</th>\n",
       "      <th>val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>121</td>\n",
       "      <td>22628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>123</td>\n",
       "      <td>9056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>125</td>\n",
       "      <td>19838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>127</td>\n",
       "      <td>17153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>129</td>\n",
       "      <td>12472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>131</td>\n",
       "      <td>12739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>133</td>\n",
       "      <td>17026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>135</td>\n",
       "      <td>43142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>137</td>\n",
       "      <td>51146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>139</td>\n",
       "      <td>20433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>141</td>\n",
       "      <td>28605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>143</td>\n",
       "      <td>19625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>145</td>\n",
       "      <td>15923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>147</td>\n",
       "      <td>19918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>149</td>\n",
       "      <td>13892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>151</td>\n",
       "      <td>9790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>153</td>\n",
       "      <td>14807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>155</td>\n",
       "      <td>36421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>157</td>\n",
       "      <td>539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>159</td>\n",
       "      <td>19192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>121</td>\n",
       "      <td>23068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>123</td>\n",
       "      <td>8626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>125</td>\n",
       "      <td>18541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>127</td>\n",
       "      <td>16062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>129</td>\n",
       "      <td>11330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>131</td>\n",
       "      <td>11529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>133</td>\n",
       "      <td>15241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>135</td>\n",
       "      <td>43571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>137</td>\n",
       "      <td>51041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>139</td>\n",
       "      <td>18790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>141</td>\n",
       "      <td>30367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>143</td>\n",
       "      <td>16595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>145</td>\n",
       "      <td>15829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>147</td>\n",
       "      <td>16263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>149</td>\n",
       "      <td>13723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>151</td>\n",
       "      <td>9255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>153</td>\n",
       "      <td>16498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>155</td>\n",
       "      <td>43327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>157</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>159</td>\n",
       "      <td>33428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>121</td>\n",
       "      <td>26042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>123</td>\n",
       "      <td>9779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>125</td>\n",
       "      <td>20905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>127</td>\n",
       "      <td>18634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>129</td>\n",
       "      <td>13045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>131</td>\n",
       "      <td>15690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>133</td>\n",
       "      <td>19898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>135</td>\n",
       "      <td>44107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>137</td>\n",
       "      <td>42945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>139</td>\n",
       "      <td>18879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>141</td>\n",
       "      <td>32663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>143</td>\n",
       "      <td>19089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>145</td>\n",
       "      <td>20594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>147</td>\n",
       "      <td>23784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>149</td>\n",
       "      <td>16133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>151</td>\n",
       "      <td>15874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>153</td>\n",
       "      <td>17764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>155</td>\n",
       "      <td>35743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>157</td>\n",
       "      <td>563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>159</td>\n",
       "      <td>22113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>620 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    TRADE_DATE_DAY  TRADE_ADDRESS    val\n",
       "0       2015-08-01            121  22628\n",
       "1       2015-08-01            123   9056\n",
       "2       2015-08-01            125  19838\n",
       "3       2015-08-01            127  17153\n",
       "4       2015-08-01            129  12472\n",
       "5       2015-08-01            131  12739\n",
       "6       2015-08-01            133  17026\n",
       "7       2015-08-01            135  43142\n",
       "8       2015-08-01            137  51146\n",
       "9       2015-08-01            139  20433\n",
       "10      2015-08-01            141  28605\n",
       "11      2015-08-01            143  19625\n",
       "12      2015-08-01            145  15923\n",
       "13      2015-08-01            147  19918\n",
       "14      2015-08-01            149  13892\n",
       "15      2015-08-01            151   9790\n",
       "16      2015-08-01            153  14807\n",
       "17      2015-08-01            155  36421\n",
       "18      2015-08-01            157    539\n",
       "19      2015-08-01            159  19192\n",
       "20      2015-08-02            121  23068\n",
       "21      2015-08-02            123   8626\n",
       "22      2015-08-02            125  18541\n",
       "23      2015-08-02            127  16062\n",
       "24      2015-08-02            129  11330\n",
       "25      2015-08-02            131  11529\n",
       "26      2015-08-02            133  15241\n",
       "27      2015-08-02            135  43571\n",
       "28      2015-08-02            137  51041\n",
       "29      2015-08-02            139  18790\n",
       "..             ...            ...    ...\n",
       "590     2015-08-30            141  30367\n",
       "591     2015-08-30            143  16595\n",
       "592     2015-08-30            145  15829\n",
       "593     2015-08-30            147  16263\n",
       "594     2015-08-30            149  13723\n",
       "595     2015-08-30            151   9255\n",
       "596     2015-08-30            153  16498\n",
       "597     2015-08-30            155  43327\n",
       "598     2015-08-30            157    564\n",
       "599     2015-08-30            159  33428\n",
       "600     2015-08-31            121  26042\n",
       "601     2015-08-31            123   9779\n",
       "602     2015-08-31            125  20905\n",
       "603     2015-08-31            127  18634\n",
       "604     2015-08-31            129  13045\n",
       "605     2015-08-31            131  15690\n",
       "606     2015-08-31            133  19898\n",
       "607     2015-08-31            135  44107\n",
       "608     2015-08-31            137  42945\n",
       "609     2015-08-31            139  18879\n",
       "610     2015-08-31            141  32663\n",
       "611     2015-08-31            143  19089\n",
       "612     2015-08-31            145  20594\n",
       "613     2015-08-31            147  23784\n",
       "614     2015-08-31            149  16133\n",
       "615     2015-08-31            151  15874\n",
       "616     2015-08-31            153  17764\n",
       "617     2015-08-31            155  35743\n",
       "618     2015-08-31            157    563\n",
       "619     2015-08-31            159  22113\n",
       "\n",
       "[620 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_08_train=data_08_re.groupby([\"TRADE_DATE_DAY\",\"TRADE_ADDRESS\"],as_index=False)[\"val\"].count()\n",
    "data_08_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_08_train.to_csv('data_08_train.csv',sep=',',index=False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRADE_DATE_DAY</th>\n",
       "      <th>TRADE_ADDRESS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>121</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>133</td>\n",
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       "      <th>4</th>\n",
       "      <td>2015-09-01</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  TRADE_DATE_DAY  TRADE_ADDRESS\n",
       "0     2015-09-01            121\n",
       "1     2015-09-01            145\n",
       "2     2015-09-01            133\n",
       "3     2015-09-01            133\n",
       "4     2015-09-01            133"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_09_re=data_09[['TRADE_DATE_DAY','TRADE_ADDRESS']]\n",
    "data_09_re.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "  TRADE_DATE_DAY  TRADE_ADDRESS  VAL\n",
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       "2     2015-09-01            133    1\n",
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   "execution_count": 33,
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       "      <td>18044</td>\n",
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       "      <td>2015-09-01</td>\n",
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       "      <td>11703</td>\n",
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       "      <th>22</th>\n",
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       "      <td>125</td>\n",
       "      <td>24442</td>\n",
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       "      <th>23</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>127</td>\n",
       "      <td>21776</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>129</td>\n",
       "      <td>14843</td>\n",
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       "      <th>25</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>131</td>\n",
       "      <td>18204</td>\n",
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       "      <th>26</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>133</td>\n",
       "      <td>22234</td>\n",
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       "      <td>2015-09-02</td>\n",
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       "      <td>53131</td>\n",
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       "      <td>141</td>\n",
       "      <td>33163</td>\n",
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       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>143</td>\n",
       "      <td>19875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>145</td>\n",
       "      <td>20047</td>\n",
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       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>2015-09-29</td>\n",
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       "      <td>23520</td>\n",
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       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>149</td>\n",
       "      <td>16286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>151</td>\n",
       "      <td>15457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>153</td>\n",
       "      <td>17034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>155</td>\n",
       "      <td>40227</td>\n",
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       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>157</td>\n",
       "      <td>513</td>\n",
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       "      <th>579</th>\n",
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       "      <th>580</th>\n",
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       "      <td>121</td>\n",
       "      <td>36567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>123</td>\n",
       "      <td>13724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>125</td>\n",
       "      <td>29356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>127</td>\n",
       "      <td>27299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>129</td>\n",
       "      <td>16106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>131</td>\n",
       "      <td>21006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>133</td>\n",
       "      <td>26670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>135</td>\n",
       "      <td>93644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>137</td>\n",
       "      <td>77351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>139</td>\n",
       "      <td>24263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>141</td>\n",
       "      <td>44796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>143</td>\n",
       "      <td>24591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>145</td>\n",
       "      <td>23656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>147</td>\n",
       "      <td>25899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>149</td>\n",
       "      <td>20317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>151</td>\n",
       "      <td>18027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>153</td>\n",
       "      <td>21325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>155</td>\n",
       "      <td>63910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>157</td>\n",
       "      <td>2043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>159</td>\n",
       "      <td>68126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    TRADE_DATE_DAY  TRADE_ADDRESS    VAL\n",
       "0       2015-09-01            121  23957\n",
       "1       2015-09-01            123   9386\n",
       "2       2015-09-01            125  20338\n",
       "3       2015-09-01            127  18993\n",
       "4       2015-09-01            129  13430\n",
       "5       2015-09-01            131  15451\n",
       "6       2015-09-01            133  19610\n",
       "7       2015-09-01            135  42363\n",
       "8       2015-09-01            137  45566\n",
       "9       2015-09-01            139  18318\n",
       "10      2015-09-01            141  32380\n",
       "11      2015-09-01            143  18664\n",
       "12      2015-09-01            145  20885\n",
       "13      2015-09-01            147  24023\n",
       "14      2015-09-01            149  16628\n",
       "15      2015-09-01            151  15282\n",
       "16      2015-09-01            153  18044\n",
       "17      2015-09-01            155  35438\n",
       "18      2015-09-01            157    574\n",
       "19      2015-09-01            159  22954\n",
       "20      2015-09-02            121  28528\n",
       "21      2015-09-02            123  11703\n",
       "22      2015-09-02            125  24442\n",
       "23      2015-09-02            127  21776\n",
       "24      2015-09-02            129  14843\n",
       "25      2015-09-02            131  18204\n",
       "26      2015-09-02            133  22234\n",
       "27      2015-09-02            135  59336\n",
       "28      2015-09-02            137  53131\n",
       "29      2015-09-02            139  21261\n",
       "..             ...            ...    ...\n",
       "570     2015-09-29            141  33163\n",
       "571     2015-09-29            143  19875\n",
       "572     2015-09-29            145  20047\n",
       "573     2015-09-29            147  23520\n",
       "574     2015-09-29            149  16286\n",
       "575     2015-09-29            151  15457\n",
       "576     2015-09-29            153  17034\n",
       "577     2015-09-29            155  40227\n",
       "578     2015-09-29            157    513\n",
       "579     2015-09-29            159  34595\n",
       "580     2015-09-30            121  36567\n",
       "581     2015-09-30            123  13724\n",
       "582     2015-09-30            125  29356\n",
       "583     2015-09-30            127  27299\n",
       "584     2015-09-30            129  16106\n",
       "585     2015-09-30            131  21006\n",
       "586     2015-09-30            133  26670\n",
       "587     2015-09-30            135  93644\n",
       "588     2015-09-30            137  77351\n",
       "589     2015-09-30            139  24263\n",
       "590     2015-09-30            141  44796\n",
       "591     2015-09-30            143  24591\n",
       "592     2015-09-30            145  23656\n",
       "593     2015-09-30            147  25899\n",
       "594     2015-09-30            149  20317\n",
       "595     2015-09-30            151  18027\n",
       "596     2015-09-30            153  21325\n",
       "597     2015-09-30            155  63910\n",
       "598     2015-09-30            157   2043\n",
       "599     2015-09-30            159  68126\n",
       "\n",
       "[600 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_09_train=data_09_re.groupby([\"TRADE_DATE_DAY\",\"TRADE_ADDRESS\"],as_index=False)[\"VAL\"].count()\n",
    "data_09_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_09_train.to_csv('data_09_train.csv',sep=',',index=False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
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       "      <td>153</td>\n",
       "      <td>14822</td>\n",
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       "      <td>18839</td>\n",
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       "      <th>595</th>\n",
       "      <td>2015-10-30</td>\n",
       "      <td>151</td>\n",
       "      <td>18130</td>\n",
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       "      <th>596</th>\n",
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       "      <td>153</td>\n",
       "      <td>19550</td>\n",
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       "      <td>155</td>\n",
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       "      <td>157</td>\n",
       "      <td>555</td>\n",
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       "      <td>25100</td>\n",
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       "      <td>127</td>\n",
       "      <td>25238</td>\n",
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       "      <td>129</td>\n",
       "      <td>15317</td>\n",
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       "      <td>17790</td>\n",
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       "      <td>24695</td>\n",
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       "      <td>135</td>\n",
       "      <td>57484</td>\n",
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       "    <tr>\n",
       "      <th>608</th>\n",
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       "      <td>137</td>\n",
       "      <td>96164</td>\n",
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       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>139</td>\n",
       "      <td>28588</td>\n",
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       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>141</td>\n",
       "      <td>41325</td>\n",
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       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>143</td>\n",
       "      <td>24968</td>\n",
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       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>145</td>\n",
       "      <td>18794</td>\n",
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       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>147</td>\n",
       "      <td>19900</td>\n",
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       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>149</td>\n",
       "      <td>16387</td>\n",
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       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>151</td>\n",
       "      <td>12612</td>\n",
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       "    <tr>\n",
       "      <th>616</th>\n",
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       "      <td>18479</td>\n",
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       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>155</td>\n",
       "      <td>43181</td>\n",
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       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>157</td>\n",
       "      <td>772</td>\n",
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      ],
      "text/plain": [
       "    TRADE_DATE_DAY  TRADE_ADDRESS    VAL\n",
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       "9       2015-10-01            139  22709\n",
       "10      2015-10-01            141  33349\n",
       "11      2015-10-01            143  18373\n",
       "12      2015-10-01            145  15689\n",
       "13      2015-10-01            147  14861\n",
       "14      2015-10-01            149  14322\n",
       "15      2015-10-01            151   9563\n",
       "16      2015-10-01            153  14822\n",
       "17      2015-10-01            155  61808\n",
       "18      2015-10-01            157   1571\n",
       "19      2015-10-01            159  55737\n",
       "20      2015-10-02            121  28867\n",
       "21      2015-10-02            123   8293\n",
       "22      2015-10-02            125  19287\n",
       "23      2015-10-02            127  17537\n",
       "24      2015-10-02            129  11908\n",
       "25      2015-10-02            131  12298\n",
       "26      2015-10-02            133  14721\n",
       "27      2015-10-02            135  55837\n",
       "28      2015-10-02            137  70867\n",
       "29      2015-10-02            139  20686\n",
       "..             ...            ...    ...\n",
       "590     2015-10-30            141  42687\n",
       "591     2015-10-30            143  25110\n",
       "592     2015-10-30            145  22223\n",
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       "599     2015-10-30            159  50314\n",
       "600     2015-10-31            121  34321\n",
       "601     2015-10-31            123  12496\n",
       "602     2015-10-31            125  25100\n",
       "603     2015-10-31            127  25238\n",
       "604     2015-10-31            129  15317\n",
       "605     2015-10-31            131  17790\n",
       "606     2015-10-31            133  24695\n",
       "607     2015-10-31            135  57484\n",
       "608     2015-10-31            137  96164\n",
       "609     2015-10-31            139  28588\n",
       "610     2015-10-31            141  41325\n",
       "611     2015-10-31            143  24968\n",
       "612     2015-10-31            145  18794\n",
       "613     2015-10-31            147  19900\n",
       "614     2015-10-31            149  16387\n",
       "615     2015-10-31            151  12612\n",
       "616     2015-10-31            153  18479\n",
       "617     2015-10-31            155  43181\n",
       "618     2015-10-31            157    772\n",
       "619     2015-10-31            159  69917\n",
       "\n",
       "[620 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_10_re=data_10[['TRADE_DATE_DAY','TRADE_ADDRESS']]\n",
    "data_10_re['VAL']=1\n",
    "data_10_train=data_10_re.groupby([\"TRADE_DATE_DAY\",\"TRADE_ADDRESS\"],as_index=False)[\"VAL\"].count()\n",
    "data_10_train.to_csv('data_10_train.csv',sep=',',index=False,header=True)\n",
    "data_10_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
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       "      <td>2015-11-01</td>\n",
       "      <td>133</td>\n",
       "      <td>24603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>135</td>\n",
       "      <td>68931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>137</td>\n",
       "      <td>96821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>139</td>\n",
       "      <td>26147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>141</td>\n",
       "      <td>42065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>143</td>\n",
       "      <td>23477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>145</td>\n",
       "      <td>18426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>147</td>\n",
       "      <td>16711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>149</td>\n",
       "      <td>16012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>151</td>\n",
       "      <td>11164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>153</td>\n",
       "      <td>19881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>155</td>\n",
       "      <td>52803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>157</td>\n",
       "      <td>626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>159</td>\n",
       "      <td>78495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>121</td>\n",
       "      <td>26590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>123</td>\n",
       "      <td>10976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>125</td>\n",
       "      <td>21041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>127</td>\n",
       "      <td>20305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>129</td>\n",
       "      <td>14783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>131</td>\n",
       "      <td>16888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>133</td>\n",
       "      <td>21763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>135</td>\n",
       "      <td>43606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>137</td>\n",
       "      <td>47675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>139</td>\n",
       "      <td>20181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>141</td>\n",
       "      <td>35341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>143</td>\n",
       "      <td>21790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>145</td>\n",
       "      <td>17707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>147</td>\n",
       "      <td>17227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>149</td>\n",
       "      <td>15718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>151</td>\n",
       "      <td>11295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>153</td>\n",
       "      <td>18778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>155</td>\n",
       "      <td>43049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>157</td>\n",
       "      <td>2975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>159</td>\n",
       "      <td>49936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>121</td>\n",
       "      <td>25948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>123</td>\n",
       "      <td>12483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>125</td>\n",
       "      <td>22770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>127</td>\n",
       "      <td>21429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>129</td>\n",
       "      <td>15596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>131</td>\n",
       "      <td>17664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>133</td>\n",
       "      <td>22763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>135</td>\n",
       "      <td>43583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>137</td>\n",
       "      <td>43602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>139</td>\n",
       "      <td>21275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>141</td>\n",
       "      <td>37865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>143</td>\n",
       "      <td>24791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>145</td>\n",
       "      <td>22240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>147</td>\n",
       "      <td>26440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>149</td>\n",
       "      <td>18469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>151</td>\n",
       "      <td>17658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>153</td>\n",
       "      <td>20115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>155</td>\n",
       "      <td>38111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>157</td>\n",
       "      <td>752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>159</td>\n",
       "      <td>26994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    TRADE_DATE_DAY  TRADE_ADDRESS    VAL\n",
       "0       2015-11-01            121  41315\n",
       "1       2015-11-01            123  13417\n",
       "2       2015-11-01            125  24788\n",
       "3       2015-11-01            127  25744\n",
       "4       2015-11-01            129  15944\n",
       "5       2015-11-01            131  18443\n",
       "6       2015-11-01            133  24603\n",
       "7       2015-11-01            135  68931\n",
       "8       2015-11-01            137  96821\n",
       "9       2015-11-01            139  26147\n",
       "10      2015-11-01            141  42065\n",
       "11      2015-11-01            143  23477\n",
       "12      2015-11-01            145  18426\n",
       "13      2015-11-01            147  16711\n",
       "14      2015-11-01            149  16012\n",
       "15      2015-11-01            151  11164\n",
       "16      2015-11-01            153  19881\n",
       "17      2015-11-01            155  52803\n",
       "18      2015-11-01            157    626\n",
       "19      2015-11-01            159  78495\n",
       "20      2015-11-02            121  26590\n",
       "21      2015-11-02            123  10976\n",
       "22      2015-11-02            125  21041\n",
       "23      2015-11-02            127  20305\n",
       "24      2015-11-02            129  14783\n",
       "25      2015-11-02            131  16888\n",
       "26      2015-11-02            133  21763\n",
       "27      2015-11-02            135  43606\n",
       "28      2015-11-02            137  47675\n",
       "29      2015-11-02            139  20181\n",
       "..             ...            ...    ...\n",
       "570     2015-11-29            141  35341\n",
       "571     2015-11-29            143  21790\n",
       "572     2015-11-29            145  17707\n",
       "573     2015-11-29            147  17227\n",
       "574     2015-11-29            149  15718\n",
       "575     2015-11-29            151  11295\n",
       "576     2015-11-29            153  18778\n",
       "577     2015-11-29            155  43049\n",
       "578     2015-11-29            157   2975\n",
       "579     2015-11-29            159  49936\n",
       "580     2015-11-30            121  25948\n",
       "581     2015-11-30            123  12483\n",
       "582     2015-11-30            125  22770\n",
       "583     2015-11-30            127  21429\n",
       "584     2015-11-30            129  15596\n",
       "585     2015-11-30            131  17664\n",
       "586     2015-11-30            133  22763\n",
       "587     2015-11-30            135  43583\n",
       "588     2015-11-30            137  43602\n",
       "589     2015-11-30            139  21275\n",
       "590     2015-11-30            141  37865\n",
       "591     2015-11-30            143  24791\n",
       "592     2015-11-30            145  22240\n",
       "593     2015-11-30            147  26440\n",
       "594     2015-11-30            149  18469\n",
       "595     2015-11-30            151  17658\n",
       "596     2015-11-30            153  20115\n",
       "597     2015-11-30            155  38111\n",
       "598     2015-11-30            157    752\n",
       "599     2015-11-30            159  26994\n",
       "\n",
       "[600 rows x 3 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_11_re=data_11[['TRADE_DATE_DAY','TRADE_ADDRESS']]\n",
    "data_11_re['VAL']=1\n",
    "data_11_train=data_11_re.groupby([\"TRADE_DATE_DAY\",\"TRADE_ADDRESS\"],as_index=False)[\"VAL\"].count()\n",
    "data_11_train.to_csv('data_11_train.csv',sep=',',index=False,header=True)\n",
    "data_11_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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